Escalonamento genético FJSP com tempo de configuração dependente de seqüência

Abstract

A Job Shop Problem (JSP) may be considered a hard combinatorial optimization problem. Although the various problem solution methods and processors technological evolutions, scheduling problems still figure out as hard to solve problem, mainly due its combinatorial nature which characterize them as NP-Complete Problems. This work present a genetic algorithm method to solve Flexible Job Shop problem, an extension of JSP, which major aspects involves the separable sequence-dependent setup time and multi- objective optimization. There are several environments where the need of the best scheduling or sequencing of activities exists. Workflow environments frequently present this need of optimal performance for activity sequencing for each resource. In that way, this work also presents the integration of scheduling FJSP to a workflow through a process modeling architecture know as Genetic Workflow, a tool that may assist the definition or optimization of process modeling.